Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents
Source abstract
Abstract
By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper guidance is required to support users as they find out what they want to know and broaden their knowledge. In this paper, we validate topic recommendation strategies of system-initiative QA agents that suggest multiple topics in different ways to influence users’ information exploration, and to help users proceed to deeper levels in topics on the same subject, to offer them topics on various subjects, or to provide them with selections at random. To examine how different recommendations influence users’ experience, we have conducted a user study with 50 participants which has shown that providing recommendations on various subjects expands their interest on subjects, supports longer conversations, and increases willingness to use QA agents in the future.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Expert finding and Q&A systems · Topic Modeling · Speech and dialogue systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
Funding
National Research Foundation of Korea
References (30)
Show source-provided references
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